Cooperative Relaying Strategies for Smart Grid Communications: Bargaining Models and Solutions
Bibliographic record
Abstract
In smart grid, the frequency regulation can be provided by both the automatic generation control (AGC) and the demand-side regulation, and the regulation errors increase the electricity costs to the utility company. The demand-side regulation adopts a hierarchical communication architecture, and the data aggregator unit (DAU) may suffer from congestions which consequently increase the costs to the utility company for more AGC service except for the demand-side regulation. In this paper, we employed the base stations as relays and formulated the electricity costs-based upon the regulation errors and the packets loss model. Specifically, the utility company decides the relaying bandwidth to minimize its electricity costs, and the relay selects the base price to maximize its profits. The novelty of this paper is twofold. First, we formulate the interactions between the utility company and the relay as a bargaining problem. Second, we utilize the Nash bargaining solution (NBS) and Raiffa-Kalai-Smorodinsky (RBS) bargaining solution to achieve the Pareto-optimal outcome. Furthermore, we extended the results to the case with multiple DAUs and multiple relays. The numerical results demonstrate the cost reduction of the utility company and the profit increase of the relay under the NBS or RBS strategy. In addition, the NBS strategy can bring about more profits for the relay than the RBS strategy, while the RBS strategy can provide a fairer payoff allocation and lower costs to the utility company than the NBS strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".